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E-LEARNING SYSTEMS SUCCESS:

A THEORETICAL MODEL

By:

Maria Manuela Simões Aparício da Costa, MSc

A Dissertation submitted to Nova Information Management School,

Universidade Nova de Lisboa, in partial fulfilment of the requirements for

the

Degree of

Doctor of Philosophy in Information Management

Specialization in Information and Decision Systems

Nova Information Management School, Universidade Nova de Lisboa

July, 2016

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Copyright © by

Maria Manuela Simões Aparício da Costa

All rights reserved

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E-Learning Systems Success:

A Theoretical Model

By:

Maria Manuela Símões Aparício da Costa, MSc

Supervisors:

Professor Fernando Lucas Bação, PhD

Professor Tiago André Gonçalves Félix de Oliveira, PhD

Nova IMS, Universidade Nova de Lisboa

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who gave me their continuous attention and constructive feedback during all the PhD Program. My thankfulness to Professor Fernando Bação for suggesting this topic, for his valuable inputs of new ideas in the design of the research, for his disposal each time I needed, and for reinforcing that I should always give the best in me. My gratitude to Professor Tiago Oliveira for being there all the time, and for giving support in every detail and for strengthening the best work I could contribute. For their support and enlightening freedom of think and enthusiastic work, that I was able to deliver such accomplishments. I am also most grateful to Professor Marco Painho for his enlighten discussion and supporting every time I needed, and specially for providing me the opportunity to conduct a research on the first Nova IMS massive open online course.

I am also thankful to my wonderful colleagues, Nadine Corte-Real, Vasco Monteiro, and Carlos Vai, with whom I worked during these years with the most enthusiastic and constructive way. During the time we spent together, I felt that we supported each other in good and sad moments. I cherish all the time we passed together my friends!

My gratitude to all my colleagues and to Nova IMS professors for ensuring the Excellency of teaching and researching, emphasizing the potential of students in this most important phase. My gratitude also goes to Universidade Nova de Lisboa for supporting, especially in the Doctoral School, in which courses I had the opportunity to collaborate and learn with other PhD Students, and to develop new skills. My cordial gratitude to all my teachers, colleagues, and academic staff.

To all anonymous people, who participated on the survey, for without their contribution the empirical studies would have not been possible.

To all my family and friends for their support.

At last, but not in last, Carlos, Sofia, and João, my dearests, you have been deprived from me, by long hours, so many walks undone and so many happy moments, would have been. For all the care, support, incentive, understanding and love, my deepest gratitude. These are the remembering that I keep inside me, thank you.

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strategy in universities and organizations, due to the recent increase of e-learning systems usage in different scenarios, the way people learn and access knowledge leads to a paradigm shift. Through massive online open courses (MOOCs) individuals are empowered and able to learn about a myriad of topics and areas, allowing for the development of skills and education in a truly independent way. The new tendencies reveal that e-learnings systems and MOOCs bring more enrolments than university enrolments over centuries, and teachers may have more students attending one massive course than in all the rest of their professional life. This medium may seem to be the answer to all learning barriers, but the effect of users’ characteristics and their engagement level on the success of e-learning systems and MOOCs is yet to be explored. Understanding the determinants of e-learning success is crucial for defining instructional strategies.

The aim of this dissertation is to theorize by proposing an e-learning systems success model, which includes users’ characteristics, pedagogical perspectives, and technological aspects. This dissertation proposes three theoretical success models and report the empirical studies for each of the models’ validation. The first model determines that cultural aspects (individualism/collectivism) are success determinants in e-learning systems. The second model assesses the non-cognitive skills (stamina in pursuing long term objectives, also known as grit) and their effect on e-learning success. The third model determines that certain pedagogic strategies, such as gamification, are powerful determinants in MOOCs success. The first two studies were developed through an electronic survey distributed to higher education students belonging to various learning levels and from several universities. The third study, on the determinants of successful MOOCs, empirically measured these factors in a real MOOC context. The studies apply quantitative methods, and validate the theoretical models using structural equation modeling (SEM). The contributions of this dissertation are several, as it puts forward a theoretical framework for guiding e-learning studies. Findings demonstrate the determinant role of individualism/collectivism on individual and organizational impacts. Students influenced by collective culture perceive more individual and organizational

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information quality and system quality explain user satisfaction with e-learning systems and MOOCs. Our findings also show that gamification has a significant and decisive impact on the success of MOOCs at both an individual and organizational level. Gamification also moderates the relationship between individual impact and organizational impact.

Keywords:

E-learning systems, massive open online course, theoretical framework, MOOC, success model, culture, grit, and gamification.

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estratégia educacional das universidades e formativa das organizações. Devido ao crescente número de cenários de uso a uma mudança de paradigma no modo de aprendizagem e no modo de acesso ao conhecimento. Através dos cursos online massivamente abertos (MOOCs), os indivíduos têm acesso a aprendizagem de uma miríade de áreas e tópicos, com vista a adquirir competências de um modo independente e verdadeiramente autónomo. As novas tendências revelam que os sistemas de e-learning e as plataformas de MOOCs, trazem consigo mais alunos inscritos do que alguma vez na história das universidades, os professores têm mais alunos do que na sua vida profissional inteira. Estes meios parecem responder a todas as barreiras da aprendizagem, no entanto, os efeitos características individuais dos utilizadores e o seu nível de envolvimento no sucesso do e-learning e dos MOOCs, está ainda por explorar. Compreender os fatores de sucesso no e-learning é crucial para uma adequada definição de estratégias instrucionais. O objetivo desta tese de doutoramento é o de modelar e teorizar sobre a realiade dos sistemas de e-learning, propondo um modelo de sucesso para o e-learning, incluindo as características dos utilizadores, as perspetivas pedagógicas e os aspetos tecnológicos. Nesta tese são propostos três modelos de sucesso e são reportados aqui as respetivas validações. O primeiro modelo determina que os aspetos culturais (individualismo/coletivismo) são fatores determinantes no sucesso dos sistemas de e-learning. O segundo modelo afere que as características não cognitivas (persistência na consecução de objetivos de longo prazo) afetam o sucesso do e-learning. O terceiro modelo determina que as estratégias pedagógicas, como a gamificação, são um poderoso determinante no sucesso dos MOOCs. Os dois primeiros estudos foram desenvolvidos através de um inquérito electrónico distribuído a alunos do ensino superior universitário de vários níveis. O terceiro estudo empírico foi realizado em contexto real de um MOOC. Os estudos aplicaram métodos quantitativos e validaram os modelos teóricos através de sistemas de equações estruturais. Esta tese tem varias contribuições, apresenta um quadro conceptual teórico para guiar vários estudos de e-learning. Os resultados demonstram o papel determinante do individualismo/coletivismo nos impactes individuais e organizacionais. Demonstra que os alunos influenciados por culturas mais coletivistas

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individual, em contexto de uso de sistemas de e-learning. Os resultados demonstram que a qualidade da informação e a qualidade do sistema explicam a satisfação do utilizador em contextos de uso de sistemas de e-learning e de MOOCs. Entende-se que a gamificação tem um impacte decisivo e significativo no sucesso dos MOOCs, quer a nível de percepção de impacto individual quer organizacional. A gamificação modera também a relação positiva entre o impacte individual no impacte organizacional.

Palavras-chave: Sistemas de e-learning, cursos online massivamente abertos, quadro conceptual teórico, MOOC, modelo de sucesso, cultura, resiliência e gamificação.

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Aparicio, M., Bacao, F., & Oliveira, T. (2016). An e-Learning Theoretical Framework. Educational Technology & Society, 19 (1), 292–307. Available at:

http://www.ifets.info/journals/19_1/24.pdf

Aparicio,M., Bacao, F. & Oliveira, T. (2016). Cultural Impacts on e-Learning Systems' Success. The Internet and Higher Education, 31, 58-70, ISSN 1096-7516, http://dx.doi.org/10.1016/j.iheduc.2016.06.003.

http://www.sciencedirect.com/science/article/pii/S1096751616300367 Aparicio,M., Bacao, F. & Oliveira, T. (under peer review on a top scientific journal).

Grit in the Path to e-Learning success.

Aparicio,M., Bacao, F., Oliveira, T. & Painho, M. (under peer review on a top scientific journal). Gamification a Key Determinant of Massive Open Online Course (MOOC) Success.

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Bibliometric Study. AMCIS 2014 Proceedings, Savannah, Georgia, USA. Retrieved from http://aisel.aisnet.org/amcis2014/Posters/ISEducation/7 Aparicio, M., Bacao, F., & Oliveira, T. (2014). MOOC’s Business Models: Turning

Black Swans into Gray Swans. In Proceedings of the International Conference on Information Systems and Design of Communication (pp. 45–49). New York, NY, USA: ACM. doi:10.1145/2618168.2618175

Aparicio, M., & Bacao, F. (2013). “e-learning concept trends.” In Proceedings of the 2013 International Conference on Information Systems and Design of

Communication (pp. 81–86). New York, NY, USA: ACM. Doi:10.1145/2503859.2503872

Other conference papers (peer reviewed)

Aparicio, M. & Bacao, F. (2013) “Success Analysis of Collaborative Learning Systems: A Theoretical Model Proposal”, A. Rocha, Luis P. Reis, Manuel P. Cota, Marco Painho e Miguel C. Neto (Editors), Proceedings of CISTI’2013, 8th Iberian

Conference on Information Systems and Technologies, Doctoral Symposium, AISTI, ISEGI, Universidade Nova de Lisboa, 2013, Lisboa pp 324-327 Aparicio, M., & Bacao, F. (2013). “Collaborative Learning Systems Success: A

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XIX GRAPHICAL ABSTRACT ... XV PUBLICATIONS ... XVII 1. INTRODUCTION ... 1 1.1RESEARCH CONTEXT... 1 1.2MOTIVATION ... 1 1.3RESEARCH FOCUS ... 3 1.4RESEARCH GOALS ... 3 1.5RESEARCH OUTPUTS ... 4 1.6METHODOLOGICAL APPROACH ... 5

1.7RESEARCH PATH DESIGN ... 7

1.8DISSERTATION STRUCTURE ... 9

2. AN E-LEARNING THEORETICAL FRAMEWORK ... 11

2.1INTRODUCTION ... 11

2.2E-LEARNING SYSTEMS RELATED CONCEPTS ... 12

2.3LEARNING CONCEPT TRENDS ... 15

2.4E-LEARNING STUDIES ... 18

2.5 E-LEARNING SYSTEMS DIMENSIONS ... 20

2.6 E-LEARNING SYSTEMS STAKEHOLDERS... 21

2.7ELEMENTS OF AN E-LEARNING SYSTEM ... 22

2.7.1 Pedagogical models in e-learning ... 24

2.7.2 Instructional strategies ... 24

2.7.3 Learning technologies ... 25

2.8 E-LEARNING THEORY FRAMEWORK ... 26

2.9CONCLUSIONS AND FUTURE WORK ... 28

3. CULTURAL DIMENSION ON E-LEARNING SYSTEMS’ SUCCESS ... 29

3.1INTRODUCTION ... 29

3.2THEORETICAL FOUNDATIONS... 33

3.2.1 e-Learning studies ... 33

3.2.2 IS success measurement ... 35

3.2.3 Cultural factors ... 38

3.3RESEARCH MODEL & HYPOTHESES ... 39

3.4EMPIRICAL METHODOLOGY ... 44

3.4.1 Measurement instrument ... 44

3.4.2 Data collection ... 45

3.5ANALYSIS AND RESULTS ... 47

3.5.1. Assessment of the measurement model ... 47

3.6.DISCUSSION ... 51

3.6.1. Hypothesis discussion ... 51

3.6.2. Theoretical implications ... 53

3.6.3. Practical implications ... 55

3.6.4. Limitations and future research ... 56

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4.3RESEARCH MODEL AND HYPOTHESES ... 65

4.4EMPIRICAL METHODOLOGY ... 69

4.4.1 Construct operationalization ... 69

4.4.2 Data collection ... 70

4.5ANALYSIS AND RESULTS ... 72

4.5.1 Measurement model assessment ... 72

4.5.2 Structural model assessment ... 74

4.6DISCUSSION ... 77

4.6.1 Hypotheses discussion ... 77

4.6.2 Implications ... 79

4.6.3 Limitations and future research ... 80

4.7CONCLUSIONS ... 80

5. GAMIFICATION: A KEY DETERMINANT OF MASSIVE OPEN ONLINE COURSE (MOOC) SUCCESS ... 83

5.1INTRODUCTION ... 83

5.2THEORETICAL BACKGROUND ... 85

5.2.1 Earlier studies of MOOCs... 85

5.2.2 Success studies in IS ... 87

5.2.3 Gamification as a success measure in MOOCs ... 90

5.3RESEARCH MODEL AND HYPOTHESIS ... 92

5.4DATA COLLECTION AND SAMPLE CHARACTERIZATION ... 97

5.5ANALYSIS AND RESULTS ... 100

5.5.1 Assessment of measurement model ... 101

5.5.3 Structural model assessment ... 102

5.6DISCUSSION ... 106

5.6.1 Theoretical implications ... 110

5.6.2 Practical implications ... 110

5.6.3 Limitations and future work ... 111

5.7CONCLUSIONS ... 112

6. CONCLUSIONS ... 113

6.1SUMMARY OF FINDINGS ... 113

6.2CONTRIBUTIONS ... 115

6.3LIMITATIONS AND FUTURE RESEARCH ... 116

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Table 1.1 Research objectives, sub-objectives, and publications ... 4

Table 1.2 Research framework based on March & Smith (1995) ... 5

Table 1.3 Methodological approach ... 6

Table 2.1 E-learning related concepts based on Aparicio & Bacao (2013) ... 13

Table 2.2 e-Learning studies ... 19

Table 2.3 e-Learning systems stakeholders ... 21

Table 2.4 e-Learning concept perspectives overlapping ... 23

Table 2.5 Instructional strategies and learning technologies ... 25

Table 3.1 Studies on adoption and use of e-learning Systems ... 33

Table 3.2 Proposed Model Constructs ... 40

Table 3.3 Testing possible response bias: early vs. late respondents ... 46

Table 3.4 Sample characterization... 46

Table 3.5 Results of the measurement model ... 48

Table 3.6 Interconstruct correlations and square root of AVEs ... 48

Table 3.7 Results of hypotheses tests ... 51

Table 4.1 Grit earlier empirical studies on Grit ... 61

Table 4.2 Proposed model constructs ... 65

Table 4.3 Sample characteristics and representativeness ... 71

Table 4.4 Results of the measurement model ... 73

Table 4.5 Interconstruct correlations and square root of AVE (in bold on diagonal) ... 74

Table 4.6 Results of hypotheses tests ... 76

Table 5.1 MOOC studies ... 86

Table 5.2 Empirical studies using IS success dimensions ... 89

Table 5.3 Model constructs ... 92

Table 5.4 MOOC components ... 98

Table 5.5 Descriptive statistics of participants’ characteristics ... 99

Table 5.6 Quality criteria and factor loadings ... 101

Table 5.7 Factor correlation coefficients & square root of AVE (in bold on diagonal) ... 102

Table 5.8 Research models estimations ... 103

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Figure 1.3 Dissertation structure and publications ... 9 Figure 2.1 Timeline of E-learning related concepts (Aparicio, Bacao, & Oliveira, 2014b) ... 15 Figure 2.2 E-learning concepts related to the time reference ... 18 Figure 2.3 Holistic e-learning systems theoretical framework ... 27 Figure 3.1 TAM model (Davis, 1986) ... 36 Figure 3.2 Evolution of DeLone & McLean IS success model ... 37 Figure 3.3 Research Model ... 43 Figure 3.4 Results of the structural model analysis ... 50 Figure 4.1 Research Model ... 66 Figure 4.2 Results of the structural model analysis ... 75 Figure 5.1 Gamification relationship with neighbor concepts ... 90 Figure 5.2 MOOCs Success Model ... 93 Figure 5.3 Survey Respondents’ origin by countries ... 100 Figure 5.4 MOOC success model results ... 105 Figure 5.5 Gamification moderation effect ... 109

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AIS Association for Information Systems

AISTI Associação Ibérica de Sistemas e Tecnologias de Informação

ALE Artificial Learning Environments

AMCIS Americas Conference on Information Systems

AVE Average Variance Extracted

BI Behavioral Intention

B-Learning Blended Learning

CAE Computer-Assisted Education

CAI Computer-Assisted Instruction

CAL Computer-Assisted Learning

CBE Computer-Based Education

CFL Computer-Facilitated Learning

CH Challenge

CI Consistency of Interest

CISTI Conferência Ibérica de Sistemas e Tecnologias de Informação

CMI Computer-Managed Instruction

cMOOC Connective Massive Open Online Course

CoP Communities of Practice

CSCL Computer Support for Collaborative Learning

D&M DeLone & McLean

DOCC Distributed Open Collaborative Courses

DOI Diffusion of Innovation

E Enjoyment

e-Learning Electronic Learning

eLS e-Learning Systems

EuroSIGDOC European Chapter of Special Interest Group in Design of Communication

GAM Gamification

IC Individualism/Collectivism

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IQ Information Quality

IS Information Systems

ISSA Information Systems Success Antecedents

IT Information Technology

LCMS Learning Content Management Systems

KM Knowledge management

LMS Learning Management Systems

LOOC Little Open Online Course

m-Learning Mobile Learning

MOOC Massive Open Online Course

NOVA IMS Nova Information Management School

NB Net Benefits

OECD Organization for Economic Co-operation and Development

OI Organizational Impacts

PE Perseverance Effort

PEOU Perceived Ease of Use

PLS Partial Least Square

PU Perceived Usefulness

REAL Rich Environments for Active Learning

SDL Self-Directed Learning

SEM Structural Equation Modelling

SerQ Service Quality

SPOC Small Private Online Course

SRE Self-Regulatory Efficacy

SysQ System Quality

TAM Technology Acceptance Model

TPB Theory of Planned Behavior

TRA Theory of Reasoned Action

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“The mind is not a vessel that needs filling, but wood that needs igniting.” (Plutarch)

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CHAPTER I- INTRODUCTION

1. Introduction

This chapter contextualizes the research question and main research goals. Here is also described the methodological approach, and the publications and their relationship with the various research design phases are presented.

1.1 Research context

This dissertation is within the context of information management, with special emphasis in the information systems, by contributing to theory building (Jarvinen, 2000; March & Smith, 1995; Nunamaker, Chen, & Purdin, 1990). It proposes a theoretical framework and three models to be applied at an individual level. This research presents the main success factors upon which academia and industry can base their decisions in the e-learning and massive open online courses (MOOCs) contexts.

1.2 Motivation

E-learning systems adoption and usage is an important research field, not only because technological development in the last decade has accelerated the spread of those systems, but also because of the increasing adoption of e-learning systems in universities and enterprises and by individuals in general (OECD, 2012, 2014, 2015). The e-learning industry increased substantially in the last decade finding new markets and designing new business models. E-learning platforms are now used for life-long learning in universities and training within organizational contexts. Learning markets are growing throughout the world, the growth rate of on-line courses stands at 65% (Means, Toyama, Murphy, Bakia, & Jones, 2009). Over 70% of youth across OECD use information communication technologies (ICTs) for learning purposes, representing 28 million of people globally. In a year or two they will be university students, and in five to seven years they will be in the labor market. In 2013, 7.8% Europeans followed an online course. Hence, ICT use in

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education is one of the main pillars that support digital strategies in digital economies. As education plays a central role in determining the range of Internet activities (OECD, 2014, 2015). MOOCs allow teachers to lecture more students on one course than in a lifetime of classroom teaching, and universities can reach more learners in a single course than in centuries of traditional classes (Koller, 2012; Rodriguez, 2012).

Several studies have been conducted, over more than 16 years, in order to measure e-learning strategies. Some authors studied the Internet-based e-learning medium in a motivational perspective using adoption models (Chen & Liu, 2013; Davis, 1989; Lee, Cheung, & Chen, 2005). Some research studies has bought to understand the satisfaction level toward e-learning systems (Aggelidis & Chatzoglou, 2012; Kassim, Jailani, Hairuddin, & Zamzuri, 2012; Sun, Tsai, Finger, Chen, & Yeh, 2008). Other studies were conducted to understand learners’ perceptions of e-learning usage compared to traditional means of learning (Cavus, Uzonboylu, & Ibrahim, 2007; Machtmes & Asher, 2000; Zhao, Lei, Yan, & Tan, 2005). Other studies focus on course resources content (Piccoli, Rami, & Ives, 2001; Rosenberg, 2005; Zinn, 2013). Other studies address the students’ interaction in the learning process (Bain, McNaught, Mills, & Lueckenhausen, 1998; Ludvigsen & Morch, 2010). Several authors measure the success of e-learning systems courses and modules (Aggelidis & Chatzoglou, 2012; Wang & Chiu, 2011; Wang, Wang, & Shee, 2007). Some studies mention that online enrolments increased substantially in e-learning courses, especially open courses (Allison, Miller, Oliver, Michaelson, & Tiropanis, 2012; Tabarrok, 2012). Although e-learning systems is a topic that is widely studied, some studies focus more on the adoption of a specific platform, or on the attendance of a course, and others focus on learners’ satisfaction with learning platforms and contents. However, to the best of the author’s knowledge, none of these earlier e-learning systems studies determined whether individual learners’ characteristics combined with technology and instructional strategies (services provided through technology) were actual determinants to the success in e-learning. It is important to understand and identify the factors that have a positive effect on the perceived positive learners’ benefits. Modeling e-learning systems success concerns all schools, universities, and companies since these platforms are an important diffusion channel of knowledge.

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1.3 Research Focus

The new trends reveal that e-learning opens new opportunities and mitigates the digital divide problem (Cruz-Jesus, Oliveira, & Bacao, 2012). A new paradigm arises; university massive courses achieve more enrolments than centuries, worth of traditional enrolments. Teachers may have more students attending one massive course than in their entire classroom experience at the same time, bring status to Universities and to teachers (Daniel, 2012; Fox, 2013; Russell et al., 2013). These courses coexist with university e-learning courses. The focus herein is to answer the following research questions:

Why do people use e-learning systems, and what are the factors that lead to the e-learning systems success?

The research object is the post-adoption usage of e-learning systems, at an individual level, in university, and massive online open courses contexts.

1.4 Research Goals

The main goals of this dissertation include the identification of the success factors of e-learning and MOOC, and building and validating of a theoretical model. The results of this dissertation were disseminated in computer science conferences and, peer reviewed journal articles. The main research goals are as follows:

1. (RO1) Identify the motivations that lead people to adopt e-learning systems and MOOCs;

2. (RO2) Identify the factors that lead to e-learning systems success and MOOCs; 3. (RO3) Identify the users’ satisfaction level about e-learning systems and MOOCs; 4. (RO4) Identify the net benefits of e-learning systems and MOOCs;

5. (RO5) Build e-learning and MOOC success models; 6. (RO6) Validate the theoretical models;

7. (RO7) Publish the results.

Table 1.1 shows the relationship between the main research goals (first column), and their study contexts (second column). The main goals and their study contexts are related to

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the sub-goals (second line), and the main dissemination works (first line); e.g. the first research objective, referring to the identification of learners’ motivations in the adoption of e-learning systems, was accomplished in article one; article two; and article three for the e-learnings context, and in article four for the MOOC context. The first main goal is achieved through literature review, reported in all publications.

Table 1.1 Research objectives, sub-objectives, and publications

Article 1 Article 2 Article 3 Article 4 Study Context Literature review on e-learning

systems Understand the impact of individuals cultural differences on e-learning systems success Understanding the impact of users’ individual characteristics on e-learning systems success Understanding the MOOCs’ success determinants RO1 University e-learning MOOC    RO2 University e-learning MOOC    RO3 University e-learning MOOC   RO4 University e-learning MOOC   RO5 University e-learning MOOC   RO6 University e-learning MOOC   RO7 University e-learning MOOC    Note: RO- Research objective

1.5 Research outputs

The research outputs (Table 1.2) are based on March & Smith’s (1995) research framework. This dissertation presents the concepts of e-learning systems, which as constructs that are defined as “concepts form the vocabulary of a domain. They constitute a conceptualization used to describe problems within the domain and to specify their solutions” (March & Smith, 1995, p. 256). Research outputs are e-learning systems and MOOC success models that will establish a set of propositions among the constructs identified.

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Table 1.2 Research framework based on March & Smith (1995)

Research Activity

Design Science Natural Science

Build Evaluate Theorize Justify

Research Outputs

Constructs e-learning/MOCC systems success constructs

Model e-learning/MOOC systems success model Method

Instantiation

1.6 Methodological approach

This dissertation’s methodological approach follows a positivist philosophy. It presents all the research based on scientific literature, deduces research hypotheses, and validates those hypotheses through empirical work (Orlikowski & Baroudi, 1991; Straub, Boudreau, & Gefen, 2004). The research outputs are transposed to a research approach based on Jarvinen’s (2000) taxonomies, from which a number of research methods are applied to the research questions, as in Figure 1.1. These taxonomies typify computer science research outputs. The approach is to study the reality mainly in a conceptual and analytical approach.

Figure 1.1 Research approaches based on the Jarvinen’s (2000) taxonomies The methodological approach is composed of two main parts (Table 1.3). The first part of the methodology is a literature review on e-learning systems and MOOCs. The second

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part of the methodological approach consists of three empirical works. These empirical works correspond to three quantitative studies for validating the hypotheses and models.

Table 1.3 Methodological approach

Objective Method Instruments Understand various e-learning concepts and

dimensions Literature review Scientific papers & articles Understand e-learning trends Bibliometric study

Scientific digital libraries search engines & Software for visualizing data (Circos.ca) Identify the factors that lead to e-learning

systems/MOOC success; Literature review on e-learning studies

Scientific papers & articles

Identify the users’ satisfaction factors on

e-learning systems/MOOC Literature review on e-learning studies Identify the net benefits of e-learning

systems/MOOC

Literature review on e-learning studies & on information systems success/MOOCs Construct theoretical e-learning/ MOOC success

models Literature review Hypotheses testing

Testing of e-learning/ MOOC success models Structured equation modeling (SEM) using partial least squares (PLS)

Questionnaires & Statistical software for equation modeling (SmartPLS)

Figure 1.2 depicts the three empirical studies and the way those models articulate with each other. The empirical studies took place in two different contexts: (i) university e-learning systems, (ii) MOOC real context (Nova IMS, 2015).

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Figure 1.2 Empirical studies

The first and second empirical studies propose two success models in a universities e-learning systems context. These models integrate information systems success theory with learners’ individual characteristics. These studies are presented in detail in chapters 3 and 4 of this dissertation. The third empirical study, which is in a real MOOC context (Nova IMS, 2015; Oliveira & Painho, 2015), integrates information systems success theory with a new construct, gamification.

1.7 Research path design

This dissertation draws upon the findings of various studies, disseminated separately in various scientific peer reviewed publications, four conference papers, and four journal articles. The research started with the definition of the research question and the identification of the main research goals. At that phase the design of the methodological approach was developed, including the main research phases and corresponding scientific

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methods. At this stage we presented a paper to a doctoral symposium of CISTI 2013, the 8th Iberian Conference of Information Systems (Aparicio & Bacao, 2013b). In the same

year the paper entitled “e-Learning concept trends” reporting a bibliographic study on the main concepts and their relationship was presented at ISDOC 2013, the International Conferences of Information Systems and Design of Communication of ACM (ISDOC 2013), in Lisbon (Aparicio & Bacao, 2013a). A study proposing two distinct business models, entitled “MOOCs’ business models: turning black swans into gray swans” (Aparicio, Bacao, & Oliveira, 2014a) was presented at ISDOC 2014, an international conference promoted by EuroSIGDOC/ACM European Chapter in Lisbon. In the same year the paper entitled “Trends in e-learning ecosystem: a bibliometric study” (Aparicio, Bacao, & Oliveira, 2014b) was presented at AMCIS 2014, the international conference of AIS, in Savannah, Georgia. These initial studies endorsed a more in-depth literature review and constituted the cornerstone of the first journal paper, the literature review that contributed with a theoretical framework on e-learning systems and MOOCs contexts. The paper, “An e-learning theoretical framework”, was submitted and accepted, after double blind peer review, for the Education Technology and Society Journal (Aparicio, Bacao, & Oliveira, 2016a). This article corresponds to the literature review chapter of this dissertation. In this publication the conceptual ecology of e-learning systems and MOOCs are reported along with a global point of view of the e-learning dimensions, stakeholders and information systems’ main components, from which a theoretical conceptual framework emerged.

After an initial literature and bibliographical study, three empirical studies were conducted in two different contexts, in a university e-learning systems usage context, and in a MOOC context. One empirical study corresponds to the sixth paper; “Cultural Dimensions on e-Learning Systems Success”, submitted and accepted for The Internet and Higher Education Journal (Aparicio, Bacao, & Oliveira, 2016b), a peer review journal. This article corresponds to the third chapter of this dissertation. This study focused on the culture impact on e-learning systems success.

Another empirical study, the seventh publication, is at the moment under peer review at a top international journal. This study is entitled “Grit in the path to e-learning success”

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(Aparicio, Bacao, & Oliveira, under peer review) and this paper is the base for chapter 4. This study’s emphasis is on the non-cognitive learners’ characteristics, which may impact on e-learning systems’ success.

Another empirical study, corresponding to the eighth paper, is also in the review process of a top international journal of information systems, and is entitled “gamification: a key determinant of MOOC success” (Aparicio, Oliveira, Bacao, & Painho, under peer review). This study is reported in chapter 5 of this dissertation. The main research motivation of this empirical study, is to report the positive impact of a learning strategy that incorporates game elements in the success of MOOCs. Figure 1.3 depicts the publication studies corresponding to each chapter of the dissertation.

Figure 1.3 Dissertation structure and publications

1.8 Dissertation structure

This dissertation is structured in six chapters as illustrated in Figure 1.3. The first chapter presents the dissertation motivation, the research focus question, the goals and the methodological approach. The second chapter introduces the literature review on e-learning systems concepts and describes a bibliometric study on the e-e-learning systems related concepts. In this chapter the e-learning dimensions upon which is presented a theoretical framework on e-learning systems are presented. The third chapter presents a

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theoretical model on e-learning systems success. This model includes the impact of the cultural dimension on the individual perception of success. This model was validated empirically, and the results are presented. In the fourth chapter we present a success model for e-learning, considering an individual characteristic as a success factor. This model was validated through an empirical study. The fifth chapter presents a success model for massive open online courses, and an empirical study conducted in a real MOOC context. The sixth chapter presents the main findings of the studies described here, as well as their main contributions, and limitations.

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2. AN E-LEARNING THEORETICAL

FRAMEWORK

E-learning systems have witnessed a usage and research increase in the past decade. This article presents the e-learning concepts ecosystem. It summarizes the various scopes on e-learning studies. Here we propose an e-learning theoretical framework. This theory framework is based upon three principal dimensions: users, technology, and services related to e-learning. This article presents an in-depth literature review on those dimensions. The article first presents the related concepts of computer use in learning across time, revealing the emergence of new trends on e-learning. The theoretical framework is a contribution for guiding e-learning studies. The article classifies the stakeholder groups and their relationship with e-learning systems. The framework shows a typology of e-learning systems’ services. This theoretical approach integrates learning strategies, technologies and stakeholders.

2.1 Introduction

E-learning unites two main areas, learning and technology. Learning is a cognitive process for achieving knowledge, and technology is an enabler of the learning process, meaning that technology is used like any other tool in the education praxis, as is a pencil or a notebook, for example. Although this seems quite simplistic and logical, a pencil is more technologically transparent tool, and its use may therefore seem more natural to many. Furthermore, technology underpins other problematic situations because it includes various dimensions. E-learning systems aggregate various tools, such as writing technologies, communication technologies, visualization, and storage. For these reasons, researchers and scientists have sought to transform e-learning systems into technically transparent tool, like a pencil or notebook. The e-learning literature is vast and continues to grow steadily (Aparicio et al., 2014b). Investigating e-learning systems’ adoption and usage reveals that continuous growth everywhere in the world, as well (OECD, 2012). The growth rate of on-line courses stands at 65% (Means et al., 2009), and some researchers suggest that at a governmental level, policies should be advocated enabling the e-learning usage (Kong et al., 2014).

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As Hart (2009, p. 28) says “reviewing the work of others you will be able to identify the methodological assumptions and the research strategies.” For these reasons, a holistic literature review is a valuable guide for researchers. However, no such overall view exists in the current literature. Consequently, the contribution of this article is threefold. First, we identify e-learning concepts ecosystem. Second, e-learning is examined from different angles; some studies are focused on how platforms operate to deliver information; others focus on the classes’ pedagogical content development, others focus on the user interaction. This article presents a broad literature review. Finally, based on the literature review we present a theoretical framework on e-learning systems.

The paper is structured in six sections: the first presents a discussion of the e-learning concept; the second presents a literature review on e-learning related concepts; the third presents the trends of the concepts, based on a bibliometric study; the fourth summarizes various e-learning studies. Several dimensions of e-learning systems, such as stakeholders, pedagogical models, instructional strategies and learning technologies, make up the fifth section. In the last section, we present the main result of this literature review, a theoretical framework for e-learning.

2.2 E-Learning systems related concepts

E-Learning systems are an evolving concept, rooted in the concept of Computer-Assisted Instruction (CAI) (Zinn, 2000). The concept of CAI first appeared in 1955 as a means of teaching problem-solving (Zinn, 2000). Table 2 .1 presents concepts related to e-learning. Computer assisted learning definitions have been studied in various ways. Some studies stress the technology while others have focused on communication (Mason & Rennie, 2006), as shown in Table 2.4. Our research reveals 23 concepts that belong to the use of computers in learning activities, used especially for learning purposes. The following table is arranged in ascending order according to the number of appearances of concepts in scholarly publications from 1960 to 2014.

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Table 2.1 E-learning related concepts based on Aparicio & Bacao (2013) Acronym Description Concept Focus Authors

CAI Computer-Assisted Instruction

Computer usage focused on programming teaching used in various fields:

mathematics, engineering, psychology, physics, business administration, statistics.

(Bernhardt, 1960) (Kemeny & Kurtz, 1967)

(Anderson, 2008) CBE Computer-Based

Education

Concept that focuses on the variety of computer uses in education.

(Barson, Levine, Smith Scholl, & Scholl, 1963) (Zinn, 2000)

CAL Computer-Assisted Learning

Focused on individuals rather than tasks. The use of computers to assist problem-solving. (Lanier, 1966) (Hart, 1981) (Levy, 1997) (Zinn, 2000) LMS Learning Management Systems

Supports registering services, tracks and delivering content to learners. It also reports learner progress and assessing results. LMS focuses on contents and teacher/student interaction.

(Becker, 1968) (Ismail, 2001) (Lee & Lee, 2008) CMI Computer-Managed

Instruction CMI stresses the teacher´s tasks.

(Molnar & Sherman, 1969)

(Zinn, 2000) CAE Computer-Assisted

Education

CAE concept refers to the use of computer for materials’ production and focuses on the students` use of the computer in learning.

(Bitzer & Others, 1970) (Zinn, 2000)

e-Learning Electronic Learning

E-Learning concept refers to learning via electronic sources, providing interactive distance learning. Use of a Web System as a way to access information available, disregarding time and space.

(White, 1983) (Morri, 1997) (Dorai, Kermani, & Stewart, 2001) (Rosenberg, 2000) (Piccoli, Ahmad, & Ives, 2001) ALE Artificial Learning

Environments

Artifacts´ usage as a mediator in learning

within a specific environment. (Fiol & Lyles, 1985)

m-Learning Mobile Learning

The first way to fight illiteracy. Pessanelli (1993) gives a futuristic approach to how learning could be in the 21st century, focusing the concept as modular plug-in school. Drumm & Groom used the concept to conceptualize a cyber mobile library. m-Learning is the focus of flexibilization in the learning class environment and the use of various learning sources.

(Darazsdi & May, 1989)

(Pesanelli, 1993) (Drumm & Groom, 1997)

(Rushby, 1998)

SRE Self-Regulatory Efficacy

Concept focused on learner’s independent assessment of self-regulatory learning ability.

(Bandura, 1994) (Joo, Bong, & Choi, 2000) CSCL Computer Support for Collaborative Learning

Concept that focuses on computers as a way to facilitate, augment, and redefine support learning in groups.

(Koschmann, 1994) (Sthal, Koschmann, & Suthers, 2006) (Ludvigsen & Morch, 2010) (Morch, 2013) REAL Rich Environments for Active Learning

Use of computer focused on student responsibility and initiative. Generative learning activities within authentic learning

(Grabinger & Dunlap, 1995)

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Acronym Description Concept Focus Authors contexts. Providing assessment strategies

and co-operative support.

Mega-University Mega-University

Concept that combines distance learning, higher education, size and use of

technology (Daniel, 1996) CFL Computer-Facilitated

Learning

Concept focused on the emulation of teacher-driven learning episodes contrasting with the constructivist approach. CFL groups the applications into functional categories and highlights the learning processes outcomes.

(Bain, McNaught, Mills, & Lueckenhausen, 1998) LCMS Learning Content Management Systems

Content Management launch pads for third party content that the organization would

purchase or outsource (Ismail, 2001)

B-Learning Blended Learning

Blended learning combines multimedia for learning purposes. This form of learning mixes different learning environments (face-to-face and distance). The aim is to

complement distance learning with face-to-face classes.

(Singh, 2003)

c-MOOC Connective MOOC

Massive open online courses based on the philosophy of connectivism and networking, autonomy, diversity, and openness. Content made by motivated and autonomous learners.

(Siemens, 2005) (Downes, 2008) (Downes, 2006) (Rodriguez, 2013) (Rodriguez, 2012) SDL Self-Directed Learning

Focus on the teaching–learning method. SDL refers to the use of individual ways of learning, using self-strategies of learning. These strategies may occur using a computer, although SDL may occur without a computer.

(Rovai, 2004) (Lee & Lee, 2008)

ILM Internet-based Learning Medium

ILM is focused in supporting and improving

student learning. (Lee et al., 2005)

MOOC Massive Open Online Course

Free diffusion of content courses to a global audience through the Web. Integrates the connectivity of social networking, the Facilitation of an acknowledged expert in the field of study, and a collection of freely accessible online resources.

(Fini, 2009) (McAuley, Stewart, Siemens, & Cormie, 2010)

(Godwin-Jones, 2012)

(Peter & Deimann, 2013)

x-MOOC MITx & EDX MOOC

Based on behaviorist pedagogy, relies on content diffusion, assignments, and peer assessment. Learning management systems with high-quality content.

(Rodriguez, 2012) (Rodriguez, 2013) (Bates, 2012) LOOC Little Open Online Course. Focus on the directed instructions from the teacher to the students. (Kolowich, 2012) SPOC Small Private Online Course MOOC usage as a supplement to classroom learning, not as a substitute to the traditional

way of teaching. (Fox, 2013)

From Figure 2.1 we see that e-learning concept was not the first term to be used in conceptualizing the use of computerized systems to enable or facilitate the learning process. In the 1960s, this concept focused on task accomplishment and thereafter focused

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more on the students. Mary Alice White coined the term “e-learning” in 1983, in a journal article entitled “Synthesis of Research on Electronic Learning.” E-learning was defined as “learning via electronic sources, such as television, computer, videodisk, teletext, videotext.” (White, 1983, p. 13). In 1997, e-learning meant an abbreviation of electronic learning, in turn meaning “an interactive distance learning” environment (Morri, 1997). Despite the use of the e-learning term, another author referred to the capacity of technologies combined with distance learning and with universities, which was named “mega-university” (Daniel, 1996). Online learning is another concept related to e-learning. Online learning can be defined as learning that takes place partially or entirely over the internet making information or knowledge available to users disregarding time restrictions or geographic proximity (Sun, Tsai, Finger, Chen, & Yeh, 2008). E-learning systems’ concepts include a technological and a functional focus, regarding the Internet possibilities in overcoming time and space issues. Figure 2.1 shows a timeline of the main e-learning concepts. Concepts are shown according to the first publication date.

Figure 2.1 Timeline of E-learning related concepts (Aparicio, Bacao, & Oliveira, 2014b)

2.3 Learning concept trends

Today the e-learning concept, apart from technology, includes learning strategies, learning methods, and lately is very much directed to the vast possibilities of content diffusion and connection. The concept trend no longer means simply the use of a computer as an artifact in the learning process. Figure 2.1 illustrates the evolution and frequency of each concept, according to searches made with the Google Scholar search

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engine. Each search was performed at five-year intervals, from 1960 to 2014, for each exact term, using double quotation operator (Figure 2.1). The chart gives a clear visualization of the evolution and trends since 1960 of the most used concepts, in terms of publication in scholarly conference papers and journal articles. In order to visualize these variables we construct a circle using an information aesthetic software (Krzywinski et al., 2009). The figure can be read as follows: if we divide the circle into semicircles we have the left hand part, with the concepts and the related publications per each concept and the right hand part with the time intervals (from 1960 to 2014). To connect these two sides of the circle we have colored ribbons, which relate each concept publication amount with the correspondent time interval. From this figure we gain the overall picture of the publication history on e-learning related concepts over time. The colored ribbons have different widths – wider indicating a greater number of publications in each concept per each time period.

Figure 2.2 was constructed with the bibliometric study of the publications, indexed in Google Scholar, for the most frequent e-learning-related concepts (on the left-hand side of the semicircle): CAI, CAL, SDL, e-learning, LMS, CSCL, among others (Aparicio et al., 2014b). CAI concept is the most used, because it appeared first and is still widely used today. From Figure 2.2 we can also see that CAI is the most mentioned concept; we can see the yellow relationship between the concept and all time intervals. CAI ribbons (yellow colored) are balanced across time, except in the 1960s and ’70s, when the concept was introduced. The other four concepts, SDL (red ribbon), CAL (pink ribbon), e-learning (blue ribbon), and LMS (orange ribbon), are of equal importance, although some of them appeared later. SDL, in red, is predominantly connected from 2005 until 2014 (Y05-09 and Y10-14). The most important CAL connections were formed from 2000 to 2014, even though the concept was used earlier. The e-learning concept, in blue, is mainly connected from 2000 until 2014. Other concepts show a relationship with the time intervals but these connections are not as strong as the others. Regarding the right-hand semicircle, it clearly shows that the earliest years, from 1960 to 1999, account for only one-third of the publications, with approximately two-thirds of all publications produced thereafter. This leads to the idea that the computers’ presence in the learning process has been explored and studied more in the last 14 years than it had been in the previous 40.

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The most often returned terms were: CAI, CAL, computer-based education (CBE), e-learning, learning management systems (LMS), self-directed learning (SDL), and massive open online courses (MOOC). All these concepts have two aspects in common: learning and computers; except the SDL concept, which derives from psychology (Bandura, 1994) and does not necessarily apply to computer usage. We found three concepts: small private online course (SPOC); little open online course (LOOC), and distributed open collaborative courses (DOCC). These concepts are yet to be studied in scientific research, and stand in contrast to MOOCs. SPOC focuses on a private audience, and is defined as a supplementary way of learning apart from regular face-to-face classes. LOOC differentiates itself from MOOC as it is based on a different pedagogical model; it provides direct instructions to students. DOCC also differentiates from MOOC in its focus on the pedagogic engagement of all actors, underlining on one hand the invisible work of teachers, and on the other the collective intelligence of scholars. The graphic that illustrates the evolution concept indicates a tendency from the individual learning to a global learning. Nowadays, e-learning can also mean massive distribution of content and global classes for all the Internet users.

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Figure 2.2 E-learning concepts related to the time reference

2.4 E-Learning studies

E-learning studies focus on several areas. Table 2.2 summarizes various examples of e-learning according to three main groups, people, technology, and services. As Leidner & Jarvenpaa (1995) say, IT impact on learning does not solve all problems, we have to take into account people and models of learning. Some studies seek to understand the adoption of e-learning systems; others assess the success of course contents; others evaluate the perceived student satisfaction of specific e-learning course environments.

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Table 2.2 e-Learning studies

e-Learning studies People Technology Services Authors Studies on course contents and

activities  

(Brox, Painho, Bacao, & Kuhn, 2004; Piccoli et al., 2001; Rosenberg, 2005; Zinn, 2000)

Studies on augmented reality in

e-learning 

(Bacca, Baldiris, Fabregat, Graf, & Kinshuk, 2014; Lee, Choi, & Park, 2009)

Studies about students´ interaction in collaborative

learning environments  

(Bain et al., 1998; Ludvigsen & Morch, 2010)

Study on cultural differences in

learning   (McLoughlin & Oliver, 1999; Yang, Kinshuk, Yu, Chen, & Huang, 2014)

Studies on the success of e-learning systems courses and

modules  

(Aggelidis & Chatzoglou, 2012; Kassim, Jailani, Hairuddin, & Zamzuri, 2012; Lee et al., 2005; Lee et al., 2009; Wang, Wang, & Shee, 2007)

Study on the Internet-based learning medium in a

motivational perspective 

(Lee, Chung, & Kim, 2013; Lee, Bharosa, Yang, Janssen, & Rao, 2011)

Studies on e-learning systems

adoption   (Chen & Liu, 2013; Lee et al., 2011) Studies on the satisfaction level

of e-learning systems usage.   (Aggelidis & Chatzoglou, 2012; Sun et al., 2008)

Studies on e-learning and digital

divide   (Chen & Liu, 2013; Cruz-Jesus et al., 2012)

Studies about trust level, satisfaction, and adoption of

e-learning.  

(Kassim et al., 2012; Thoms, Garrett, Herrera, & Ryan, 2008)

Studies on e-learning evaluation

processes   (Oliver & Herrington, 2003; Vavpotič, Žvanut, & Trobec, 2013)

Studies on MOOCs´ business

models  

(Aparicio et al., 2014a; Belleflamme & Jacqmin, 2014; Dellarocas & Van Alstyne, 2013)

From Table 2.2 we see that even if the study addresses students’ adoption or satisfaction, the contents, or even the way courses are designed and distributed, we can group those studies and find overlaps among them. This leads to the idea that when studying e-learning, researchers have to include variables other than technology. According to the studies examined, the way contents are delivered and the underlying learning strategies also play important roles in e-learning studies.

Apart from these dimensions, recent disruptive conditions have brought a massive diffusion of online learning through various formats, from closed to open learning, and the massification of open online courses (MOOCs) has been verified. McAuley et al. (2010, p. 4) define massive online open courses as “An online phenomenon gathering momentum over the past few years; an MOOC integrates the connectivity of social networking, the facilitation of an acknowledged expert in the field of study, and a

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collection of freely accessible online resources.” Allison et al. (2012) stated that MOOCs are disrupting the learning environment due to the global free adoption and use of these open courses. Although according to a study done by Jordan (2013), students or simply public users are enrolling in different courses by the thousands, for example, one of the largest (measured by the number of enrolled students) has 180,000 and one of the smallest has 20,000. These figures demonstrate a massive quantity of students enrolled, comparing to a face-to-face university course that never reaches such numbers of students; nor does a teacher reach such a high number of students in her/his entire career.

From the above-mentioned studies one could believe that adoption is no longer a problem in e-learning, but a study by Jordan (2013) of the disruptive potential of MOOCs compares the enrolment rates with the completion rates per each course and for all of them, finding that completion rates are very low. Motivation studies can also enlighten us with the disruptive potential of MOOCs, such as, “the individuals the MOOC revolution is supposed to help the most – those without access to higher education in developing countries – are conspicuously underrepresented among the early adopters” (Christensen et al., 2013, p. 8). MOOCs allow for a massive distribution of expressed knowledge, especially for those who cannot reach universities courses, due to economic, geographic, or political reasons. As a matter of fact, according to an empirical study (Christensen et al., 2013) MOOC attracts mainly young, well-educated and employed people from developed countries.

This summary of e-learning studies maps the various areas when studying e-learning and exposes the idea that e-learning should be studied using a combination of various dimensions.

2.5 e-Learning systems dimensions

Information systems are composed of various dimensions. From a conceptual point of view the system is an artifact (Beckman, 2002), and this author considers the use of computers in education an “artificialization.” Artifacts are not only technology, but also and mostly “a complex and changing combination of people and technology” (Dahlbom, 1996, p. 43). Technology implements artifacts and information technology serves human

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purposes, providing support to several tasks (March & Smith, 1995). Within this context, we present in this section the learning systems dimensions, in order to prepare our e-learning theory framework.

2.6 e-Learning systems stakeholders

Stakeholder analysis entails the identification of internal and external groups or individuals that can directly and indirectly affect an organization (Freeman, 2010; Stoner, Freeman, & Gilbert, 1995). Stakeholder theory can be applied to other fields beyond management (Phillips, Freeman, & Wicks, 2003). Stakeholders analysis has been used in information studies to identify the systems’ users and their direct or indirect interaction (Papazafeiropoulou, Pouloudi, & Currie, 2001; Wagner, Hassanein, & Head, 2008). We summarize the stakeholders of e-learning systems in Table 2.3.

Table 2.3 e-Learning systems stakeholders

Stakeholders Group Direct Action Internal External

Students Customers

Employers Customers

Educational Institutions Suppliers Accreditation Bodies Suppliers

Teachers Suppliers

Content Providers Suppliers

Education Ministry Board and Shareholders Teachers’ Association Professional Associations Students’ Commissions Special Interest Groups Technology Providers Suppliers

Customers are the ultimate users of the system for learning since e-learning systems are an important communication channel between learners and instructors. Learners can be individual students or company employees who are using these systems according to the development policies of their employees. In their case they are external users but they interact directly with the system. Suppliers can be schools, universities, or educational institutions in general; this stakeholder group is an internal group of users who interact directly with the system. Accreditation bodies are external; they interact directly with the system for auditing purposes. Teachers are part of the supplier group; they are internal users and interact directly with the e-learning platforms. Content providers can be internal

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or external users but they interact directly with the system. Other external stakeholders that interact directly with the e-learning systems are: education ministry, teachers’ associations, students´ commissions and technology providers. Education ministry is considered as a board and shareholder because public institutions are funded by this ministry. They have a direct interaction with the systems in order to accompany the instructional institutions in their teaching role. Teachers and student groups can also interact directly with the system if they promote learning or research activities. Although technology providers are external to the system, they can provide maintenance services to the technological part of the system by giving technical support. Each stakeholders group interacts differently with the system, although all of the stakeholders play an important role within the e-learning system activities.

2.7 Elements of an e-learning system

E-Learning theory comprises three elements. According to Dabbagh (2005) e-learning can be defined through a theory-based framework that relates learning technologies, instructional strategies, and pedagogical models or constructs. Dabbagh’s framework (2005) includes multiple dimensions, such as the way people learn (open/flexible way), with the learning strategy (collaboration, exploration, problem-solving) and also with technology. It is a pedagogical model, and “cognitive models or theoretical constructs [are] derived from knowledge acquisition models or views about cognition and knowledge, which form the basis for learning theory. In other words, they are the mechanism by which we link theory to practice” (Mehlenbacher, 2010, p. 146). Instructional strategies facilitate learning, such as, collaboration, articulation, reflection, and role-playing among others. Although they are pedagogical models, our main objective in this study is to review the literature on e-learning systems. Subsequent to Table 2.1, which presents the concepts of the context of the e-learning systems, we constructed Table 2.4 in which those concepts are classified according to two ways of e-learning definitional dimensions. First, the concepts are classified according to Dabbagh’s (2005) framework, according to whether the concepts reflect a pedagogical model, instructional strategy, or a learning technology. Second, we also identify the concepts

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according to Mason & Rennie’s (2006) classification of e-learning perspectives, whether concepts are content driven, communication focused, or technologically oriented.

Table 2.4 e-Learning concept perspectives overlapping

Year Acronym

Dabbagh’s Theory Based

Framework (2005) e-Learning Perspectives of Mason & Rennie (2006)

Pe da go gi ca l M od els In str uc tio na l St ra te gi es Le ar ni ng Te ch no lo gi es Co nt en t Co m m un ica tio n Te ch no lo gy 1960 CAI    1963 CBE    1966 CAL     1968 LMS     1969 CMI    1970 CAE    1983 e-Learning       1985 ALE   1989 m-Learning     1994 SRE      1994 CSCL       1995 REAL      1996 Mega-University     1998 CFL     2001 LCMS     2003 B-Learning       2004 SDL      2004 c-MOOC      2005 ILM     2009 MOOC       2012 x-MOOC       2012 LOOC      2013 SPOC     

Pedagogical models, instructional strategies, and learning technologies, combined together, form a framework applicable to e-learning (Dabbagh, 2005). These three components enable the linkage between who (open learning, distributed learning, or communities of practice, among others) is participating in the learning process, with the way in which these features interact (collaborating, articulation, reflecting, exploring) and the technologies through which the communication occurs (synchronous, asynchronous, communication tools, course management tools, among others).

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Pedagogical models are the basis of learning theory, as they derive from knowledge acquisition. From a pedagogical point of view these models are mechanisms that link learning theory to learning practice (Dabbagh, 2005). The pedagogical models in e-learning are open e-learning, distributed e-learning, e-learning communities, communities of practice, and knowledge building communities. The open learning can take several forms, for example, it can be a workshop, a seminar, a night course, or a distance course. Some examples on the Web are: “knowledge networks, knowledge portals, asynchronous learning networks, virtual classrooms, and telelearning” (Dabbagh, 2005, p. 30). Distributed learning is focused on the learning distribution resulting in a combined channels situation that allows learners to access education through technology or not in a way that can be obtained synchronously or asynchronously anywhere (Dabbagh, 2005). In many situations learning communities are composed of students in universities who “tend to feel more self-confident and to feel supported by peers, by instructors, and by the college” (Patterson, 2011, p. 20). Communities of practice (CoP) are defined by Wenger (1999) as informal groups of people who share the same interests on a subject. Communities of practice share interests and best practices and collaborate not only in academia but also in industry. These communities usually have regularly scheduled meetings, CoP meet face-to-face or in virtual environments (Liu, Chen, Sun, Wible, & Kuo, 2010; Wenger, 1999). A knowledge building community is perceived as a group having “commitment among its members to invest their resources in the collective, upgrading of knowledge” (Hewitt & Scardamalia, 1998, p. 82). These communities pursue the creation of knowledge by sharing individual knowledge in order to achieve learning. The pedagogical models applied to e-learning are supported in the following attributes: learning is a social process, learning in group is fundamental to achieve knowledge; distance is unimportant (space questions are blurred); teaching and learning can be segregated in time and space.

2.7.2 Instructional strategies

Instructional strategies operationalize the pedagogical models, since strategies consist of general approaches to a learning model, which is to say, the instructional. Jonassen et al.

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(1997) present five instructional strategies that, in fact, are plans and techniques that the instructor uses in order to engage the learners – in other words; instructional strategies are enablers to learning. The authors state that instructional strategies differ from learning strategies, as learning strategies are mental tools that students use to understand and learn more (Jonassen et al., 1997). The authors state that each instructional condition should meet a different instructional strategy.

2.7.3 Learning technologies

Many authors have defined the characteristics of the learning technologies to support a learning environment of collaboration and supported learning, and have left room for various perspectives (Dabbagh, 2005; Hsieh & Cho, 2011; McLoughlin & Oliver, 1999; Oliver & Herrington, 2003; Rourke & Anderson, 2002). A constructivist epistemological point of view (Hannafin, Hannafin, Land, & Oliver, 1997) requires integrated strategies, aligning several foundations and environments: psychological, pedagogical, cultural, pragmatic, and technological, since according to the characteristics of this vision “knowledge depends on the knower’s frame of reference” (Dabbagh, 2005, p. 29). Oliver and Herrigton (2003) construct an e-learning framework composed of technological elements grouped into three main areas in learning: resources, supports, and activities. Table 2.5 summarizes these instructional strategies and the correspondent technologies’ functionalities.

Table 2.5Instructional strategies and learning technologies

Strategies Technologies Au th en tic A ct iv iti es Pr ob lem S ol vi ng Ro le Pl ay in g Ar tic ul at io n & R ef le ct io n Co lla bo ra tio n & Ne go tia tio n M ul ti-pe rs pe ct iv es M od eli ng & E xp la in in g Sc af fo ld in g Authors Graphics  (Dabbagh, 2005; Hannafin et al., 1997) Digital audio & video components 

Animation 

Hypermedia 

Referências

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